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  <h1>Source code for torch.cuda.streams</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">ctypes</span>
<span class="kn">import</span> <span class="nn">torch</span>


<div class="viewcode-block" id="Stream"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Stream">[docs]</a><span class="k">class</span> <span class="nc">Stream</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_CudaStreamBase</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Wrapper around a CUDA stream.</span>

<span class="sd">    A CUDA stream is a linear sequence of execution that belongs to a specific</span>
<span class="sd">    device, independent from other streams.  See :ref:`cuda-semantics` for</span>
<span class="sd">    details.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        device(torch.device or int, optional): a device on which to allocate</span>
<span class="sd">            the stream. If :attr:`device` is ``None`` (default) or a negative</span>
<span class="sd">            integer, this will use the current device.</span>
<span class="sd">        priority(int, optional): priority of the stream. Lower numbers</span>
<span class="sd">                                 represent higher priorities.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device</span><span class="p">):</span>
            <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Stream</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="n">priority</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

<div class="viewcode-block" id="Stream.wait_event"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Stream.wait_event">[docs]</a>    <span class="k">def</span> <span class="nf">wait_event</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Makes all future work submitted to the stream wait for an event.</span>

<span class="sd">        Arguments:</span>
<span class="sd">            event (Event): an event to wait for.</span>

<span class="sd">        .. note:: This is a wrapper around ``cudaStreamWaitEvent()``: see</span>
<span class="sd">           `CUDA Stream documentation`_ for more info.</span>

<span class="sd">           This function returns without waiting for :attr:`event`: only future</span>
<span class="sd">           operations are affected.</span>

<span class="sd">        .. _CUDA Stream documentation:</span>
<span class="sd">           http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">event</span><span class="o">.</span><span class="n">wait</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>

<div class="viewcode-block" id="Stream.wait_stream"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Stream.wait_stream">[docs]</a>    <span class="k">def</span> <span class="nf">wait_stream</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stream</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Synchronizes with another stream.</span>

<span class="sd">        All future work submitted to this stream will wait until all kernels</span>
<span class="sd">        submitted to a given stream at the time of call complete.</span>

<span class="sd">        Arguments:</span>
<span class="sd">            stream (Stream): a stream to synchronize.</span>

<span class="sd">        .. note:: This function returns without waiting for currently enqueued</span>
<span class="sd">           kernels in :attr:`stream`: only future operations are affected.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">wait_event</span><span class="p">(</span><span class="n">stream</span><span class="o">.</span><span class="n">record_event</span><span class="p">())</span></div>

<div class="viewcode-block" id="Stream.record_event"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Stream.record_event">[docs]</a>    <span class="k">def</span> <span class="nf">record_event</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Records an event.</span>

<span class="sd">        Arguments:</span>
<span class="sd">            event (Event, optional): event to record. If not given, a new one</span>
<span class="sd">                will be allocated.</span>

<span class="sd">        Returns:</span>
<span class="sd">            Recorded event.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">event</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">event</span> <span class="o">=</span> <span class="n">Event</span><span class="p">()</span>
        <span class="n">event</span><span class="o">.</span><span class="n">record</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">event</span></div>

<div class="viewcode-block" id="Stream.query"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Stream.query">[docs]</a>    <span class="k">def</span> <span class="nf">query</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Checks if all the work submitted has been completed.</span>

<span class="sd">        Returns:</span>
<span class="sd">            A boolean indicating if all kernels in this stream are completed.&quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Stream</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">query</span><span class="p">()</span></div>

<div class="viewcode-block" id="Stream.synchronize"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Stream.synchronize">[docs]</a>    <span class="k">def</span> <span class="nf">synchronize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Wait for all the kernels in this stream to complete.</span>

<span class="sd">        .. note:: This is a wrapper around ``cudaStreamSynchronize()``: see</span>
<span class="sd">           `CUDA Stream documentation`_ for more info.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Stream</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span></div>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">_as_parameter_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cuda_stream</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">o</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">Stream</span><span class="p">):</span>
            <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Stream</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__eq__</span><span class="p">(</span><span class="n">o</span><span class="p">)</span>
        <span class="k">return</span> <span class="kc">False</span>

    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">hash</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">cuda_stream</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>

    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="p">(</span><span class="s1">&#39;&lt;torch.cuda.Stream device=</span><span class="si">{0}</span><span class="s1"> cuda_stream=</span><span class="si">{1:#x}</span><span class="s1">&gt;&#39;</span>
                <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">cuda_stream</span><span class="p">))</span></div>


<div class="viewcode-block" id="Event"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Event">[docs]</a><span class="k">class</span> <span class="nc">Event</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_CudaEventBase</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Wrapper around a CUDA event.</span>

<span class="sd">    CUDA events are synchronization markers that can be used to monitor the</span>
<span class="sd">    device&#39;s progress, to accurately measure timing, and to synchronize CUDA</span>
<span class="sd">    streams.</span>

<span class="sd">    The underlying CUDA events are lazily initialized when the event is first</span>
<span class="sd">    recorded or exported to another process. After creation, only streams on the</span>
<span class="sd">    same device may record the event. However, streams on any device can wait on</span>
<span class="sd">    the event.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        enable_timing (bool, optional): indicates if the event should measure time</span>
<span class="sd">            (default: ``False``)</span>
<span class="sd">        blocking (bool, optional): if ``True``, :meth:`wait` will be blocking (default: ``False``)</span>
<span class="sd">        interprocess (bool): if ``True``, the event can be shared between processes</span>
<span class="sd">            (default: ``False``)</span>

<span class="sd">    .. _CUDA Event Documentation:</span>
<span class="sd">       https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">enable_timing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">blocking</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">interprocess</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Event</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span>
            <span class="bp">cls</span><span class="p">,</span>
            <span class="n">enable_timing</span><span class="o">=</span><span class="n">enable_timing</span><span class="p">,</span> <span class="n">blocking</span><span class="o">=</span><span class="n">blocking</span><span class="p">,</span> <span class="n">interprocess</span><span class="o">=</span><span class="n">interprocess</span><span class="p">)</span>

<div class="viewcode-block" id="Event.from_ipc_handle"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Event.from_ipc_handle">[docs]</a>    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_ipc_handle</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">handle</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Reconstruct an event from an IPC handle on the given device.&quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Event</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="n">from_ipc_handle</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">handle</span><span class="p">)</span></div>

<div class="viewcode-block" id="Event.record"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Event.record">[docs]</a>    <span class="k">def</span> <span class="nf">record</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Records the event in a given stream.</span>

<span class="sd">        Uses ``torch.cuda.current_stream()`` if no stream is specified. The</span>
<span class="sd">        stream&#39;s device must match the event&#39;s device.&quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">stream</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">stream</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Event</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">record</span><span class="p">(</span><span class="n">stream</span><span class="p">)</span></div>

<div class="viewcode-block" id="Event.wait"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Event.wait">[docs]</a>    <span class="k">def</span> <span class="nf">wait</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Makes all future work submitted to the given stream wait for this</span>
<span class="sd">        event.</span>

<span class="sd">        Use ``torch.cuda.current_stream()`` if no stream is specified.&quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">stream</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">stream</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Event</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">wait</span><span class="p">(</span><span class="n">stream</span><span class="p">)</span></div>

<div class="viewcode-block" id="Event.query"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Event.query">[docs]</a>    <span class="k">def</span> <span class="nf">query</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Checks if all work currently captured by event has completed.</span>

<span class="sd">        Returns:</span>
<span class="sd">            A boolean indicating if all work currently captured by event has</span>
<span class="sd">            completed.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Event</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">query</span><span class="p">()</span></div>

<div class="viewcode-block" id="Event.elapsed_time"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Event.elapsed_time">[docs]</a>    <span class="k">def</span> <span class="nf">elapsed_time</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">end_event</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the time elapsed in milliseconds after the event was</span>
<span class="sd">        recorded and before the end_event was recorded.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Event</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">elapsed_time</span><span class="p">(</span><span class="n">end_event</span><span class="p">)</span></div>

<div class="viewcode-block" id="Event.synchronize"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Event.synchronize">[docs]</a>    <span class="k">def</span> <span class="nf">synchronize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Waits for the event to complete.</span>

<span class="sd">        Waits until the completion of all work currently captured in this event.</span>
<span class="sd">        This prevents the CPU thread from proceeding until the event completes.</span>

<span class="sd">         .. note:: This is a wrapper around ``cudaEventSynchronize()``: see</span>
<span class="sd">            `CUDA Event documentation`_ for more info.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Event</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span></div>

<div class="viewcode-block" id="Event.ipc_handle"><a class="viewcode-back" href="../../../cuda.html#torch.cuda.Event.ipc_handle">[docs]</a>    <span class="k">def</span> <span class="nf">ipc_handle</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns an IPC handle of this event. If not recorded yet, the event</span>
<span class="sd">        will use the current device. &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Event</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">ipc_handle</span><span class="p">()</span></div>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">_as_parameter_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cuda_event</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cuda_event</span><span class="p">:</span>
            <span class="k">return</span> <span class="s1">&#39;&lt;torch.cuda.Event </span><span class="si">{0:#x}</span><span class="s1">&gt;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_as_parameter_</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="s1">&#39;&lt;torch.cuda.Event uninitialized&gt;&#39;</span></div>
</pre></div>

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        $(this).closest("a").addClass("has-code");
      });
    })
  </script>
</body>
</html>